Data parallel frameworks, such as MapReduce [1]},
Hadoop [2]} and Spark [3]} are very
popular in cloud applications. In typical data center applications,
the execution process is an application consisting of multiple consecutive
stages. Each stage relies on several parallel flows, and the next
stage cannot begin until all flows in the current stage have been
transmitted. Therefore, the traditional network metrics, such as minimizing
the average flow completion time (FCT), though can guarantee the performance
at the flow level, is unable to effectively improve the transmission
performance of the application.
